Lower Resolution Levels Research Articles

Identifying jumps in high-frequency time series by wavelets

We discuss a volatility functional model and show that jumps asymptotically impact the volatility estimate. This result is useful because our model shows that significant variations affect the estimation of the volatility and historically price series have structures with this type of behavior. We also discuss a method for detecting and locating jumps at different levels and show that the jumps tend to be detected by wavelet coefficients at lower resolution levels accurately. By checking the wavelet coefficients on the different levels, we can find dyadic intervals in some levels, whose corresponding absolute value of the wavelet coefficient exceeds a threshold, and is significantly higher than the others. We applied the procedure in a simulation study and to a series of Google stocks.

Genomic epidemiology and multilevel genome typing of Bordetella pertussis

ABSTRACT Bordetella pertussis causes pertussis (or whooping cough), a severe respiratory infectious disease in infants, although it can be prevented by whole cell and acellular vaccines. The recent pertussis resurgence in industrialised countries is partly attributed to pathogen adaptation to vaccines, while emergence of antimicrobial resistance, specifically to macrolides in China, has become a concern. Surveillance of current circulating and emerging strains is therefore vital to understand the risks they pose to public health. Although the use of genomics-based typing is increasing a genomic nomenclature for this pathogen has not been well established. Here, we implemented the multilevel genome typing (MGT) system for B. pertussis with five levels of resolution, which provide targeted typing of relevant lineages and discrimination of closely related strains at the finest scale. The lower resolution levels (MGT2 and MGT3) describe the distribution of major vaccine antigen alleles including ptxP, fim3, fhaB and prn, as well as temporal and spatial trends within the B. pertussis global population. Mid-resolution levels (MGT3 and MGT4) enable typing of antibiotic-resistant lineages and Prn deficient lineages within the ptxP3 clade. The high-resolution level (MGT5) can capture finer-scale epidemiology such as outbreaks and local transmission events, with comparable resolution to existing genomic methods of strain-relatedness assessment. The scheme offers stable MGT-type assignments aiding harmonisation of typing and communication between laboratories. The scheme is available at https://mgtdb.unsw.edu.au/pertussis, is regularly updated from global data repositories and accepts public submissions. The MGT scheme provides a comprehensive, robust, and scalable system for global surveillance of B. pertussis.

CryoFold 2.0: Cryo-EM Structure Determination with MELD

Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.

Deformable registration of lung 3DCT images using an unsupervised heterogeneous multi-resolution neural network.

Lung image registration is more challenging than other organs. This is because the breath of the human body causes large deformations in the lung parenchyma and small deformations in tissues such as the pulmonary vascular. Many studies have recently used multi-resolution networks to solve the lung registration problem. However, they use the same structure of registration modules on each level, which makes it difficult to handle complex and small deformations. We propose an unsupervised heterogeneous multi-resolution network (UHMR-Net) to overcome the above problem. The image detail registration module (IDRM) is designed on the highest resolution level. Within this module, the cascaded network is used on the same resolution image to continuously learn the "remaining" detail deformation fields. The shallow shrinkage loss (SS-Loss) is designed to supervise the cascaded network, thus further improving the ability of the network to handle small deformations. Moreover, with the lightweight feature local correlation layer we proposed, the image boundary registration module (IBRM), on multiple low-resolution levels, can better solve the large deformation registration problem. The target registration error on the public DIR-Lab 4DCT dataset was 1.56 ± 1.39mm, which was significantly better than the classic conventional methods and advanced deep-based methods.

Evaluation of the Impact of Input-Data Resolution on Building-Energy Simulation Accuracy and Computational Load—A Case Study of a Low-Rise Office Building

Building-energy consumption is the primary aim of urban energy consumption, which can aid in optimization of building operation and management techniques, creating sustainable building and built environments. However, modellers’ understanding of the relationship between building-energy modelling (BEM) accuracy and computational load is still qualitative and deprived of accurate quantitative study. Based on a bottom-up engineering methodology, this study aims to quantitatively explore the effects of building-model input data with different resolution accuracies on energy simulation results, including evaluation of computational load. According to the actual parameters of the case-study building, 108 models with varying input resolution levels were developed to estimate hourly energy usage and annual mean ambient temperature. The results demonstrated that with input parameters at low resolution levels, geometric parameters such as exterior windows, interior windows, and shading exhibited significantly lower computational loads, resulting in reduced errors in the final simulation performance, whereas the occupancy schedule, thermal zoning, and HVAC configuration parameters exhibited significant declines in simulation performance and accuracy. This study presents a methodology applicable to the majority of low-rise, rectangular office structures. Future work would concentrate on carrying out comparison tests for different building forms and types while gradually improving the automation of the process to enable use of the appropriate accuracy level in assessing the crucial issue of energy-modelling input.

A robust stochastic approach in correcting the TRMM precipitation product and simulating flood features.

Logical management and decision-making on water resources require reliable weather variables, where precipitation is considered the main weather variable. Accurate estimation of precipitation is the most important topic in hydrological studies. Due to the lack of a dense network and low temporal and spatial resolution levels at ground-level rain gauges, especially in developing countries, remote sensing methods have been used widely. In recent years, a combination of satellite-ground data on precipitation has led to a more accurate insight into precipitation and improved hydrological model performance. In this study, the Kosar Dam Basin in the Khuzestan province of Iran is selected as the research zone. The TRMM satellite data is used on 50 events to analyze the satellite precipitation data. Copula theory is then employed to check the uncertainties of precipitation estimation, and new precipitations are generated through original data and bias errors. A comparison of the results of the improved TRMM, which was bias-corrected by Gaussian copula, and ground-based rainfall demonstrated the efficacy of this method, with nearly 104% and 51% improvement in the CC and RMSE performance indicators, respectively. The HEC-HMS model was used to simulate flood features based on copula-corrected precipitation over different quartiles (10%, 30%, 50%, 70%, and 90%) and rainfall duration (3, 6, 9, and 24h). The obtained R-factor values show that the associated uncertainty decreases with rainfall duration, down to 46 and 20% for discharge peak and volume, respectively. In general, the copula approach is a robust approach to improve the accuracy of the TRMM precipitation product for simulating hydrological processes.

Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation

Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels of the thermal imagery restrict the feature extraction ability of CNNs. In order to deal with these difficulties in the feature extraction, we propose a novel backbone network named Deep-IRTarget, composing of a frequency feature extractor, a spatial feature extractor and a dual-domain feature resource allocation model. Hypercomplex Infrared Fourier Transform is developed to calculate the infrared intensity saliency by designing hypercomplex representations in the frequency domain, while a convolutional neural network is invoked to extract feature maps in the spatial domain. Features from the frequency domain and spatial domain are stacked to construct Dual-domain features. To efficiently integrate and recalibrate them, we propose a Resource Allocation model for Features (RAF). The well-designed channel attention block and position attention block are used in RAF to respectively extract interdependent relationships among channel and position dimensions, and capture channel-wise and position-wise contextual information. Extensive experiments are conducted on three challenging infrared imagery databases. We achieve 10.14%, 9.1% and 8.05% improvement on mAP scores, compared to the current state of the art method on MWIR, BITIR and WCIR respectively.

THz Spectroscopic Decomposition and Analysis in Mixture Inspection Using Soft Modeling Methods

At present, the terahertz time-domain spectroscopic information of each component in multi-composition compounds detection is comprehensively combined. The lower resolution level of mixed spectra has posed many difficulties in signal analysis due to the overlapping characteristic information in the mixed ones. In this paper, to compare the performances of Nonnegative Matrix Factorization (NMF), Self-modeling Mixture Analysis (SMMA) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on complex systems, a binary mixture and a ternary mixture are employed during THz-TDS testing. The position of the absorption peak (PK) and the correlation coefficient are used to evaluate the decomposition effects. The experimental results show that the component spectra resolved by MCR-ALS demonstrate good consistency with the sample components. Further, MCR-ALS presents excellent results in comparison with NMF and SMMA in terms of decomposing precision and computing speed. MCR-ALS thus appears a promising algorithm to resolve the THz multi-way data, and may be useful for many applications, such as medicine quality assurance and unknown component identification in the general area of terahertz science and technology.

Thermal Face Recognition under Spatial Variation Conditions

This paper proposes a novel method for degraded thermal face recognition using Hu Li moments. The method deals with spatial variations of thermal images resulting from low resolution and changes in head pose. We describe thermal images with a set of scalar quantities that can capture its significant features at component level. Each thermal image is divided into components where the statistical features of these components are combined using a fusion method. The method finds a combination of multiple statistical patterns to produce an integrated result that is enhanced in terms of information content for correct classification. We show that local representations using Hu Li moments provide higher discriminability and offer robustness against variability due to spatial changes. To evaluate the performance of the proposed method, we conduct thorough experiments and detailed analysis on a database consisting of 7500 images of different poses and variable spatial resolutions. This database is generated from an initial one that originally consists of 1500 images, by down-sampling the original images to four different low resolution levels beside the original level. The results show that the proposed method achieves significantly high recognition rates over various conditions resembling practical scenarios.

Adaptive mesh refinement method for solving optimal control problems using interpolation error analysis and improved data compression

This paper presents a simple and efficient adaptive mesh refinement method for solving optimal control problems using direct methods. The method inserts new points into the mesh by successively subdividing the neighboring intervals around each mesh point where the interpolation error exceeds a prescribed threshold. The unnecessary points in the mesh are removed by the use of an improved data compression method based on generalized dyadic meshes. Furthermore, to further reduce the mesh size, the method allows the use of the neighboring mesh points of both lower and higher resolution levels (i.e., it uses more information in the mesh) when calculating the interpolation error in the mesh point insertion and compression procedures. The method is demonstrated with four examples, and its performance is compared against that of several other recently developed adaptive mesh refinement methods for optimal control reported in the literature. The numerical results show that the proposed method has advantages over other methods because it achieves the same or a more accurate solution while producing smaller mesh sizes and even requiring fewer mesh iterations than other methods.

PS1352 DISSECTING THE BONE MARROW IMMUNE MICROENVIRONMENT IN THE COMPLETE SPECTRUM OF MONOCLONAL GAMMOPATHIES: POTENTIAL IMPLICATIONS IN DISEASE PATHOGENESIS

Background: To discriminate different outcomes among patients in CR the IMWG introduced a more stringent CR (sCR) criteria by adding to the pre-existing CR criteria the requirement of a normal free-light chain ratio (sFLCr) plus the absence of clonal plasma cells (PCs) in bone marrow (BM) by immunohistochemistry (IHC). In 2011, the low-sensitivity cytometry was included as alternative methodology to IHC to define sCR. However, the prognostic significance of the kappa/lambda ratio values is not clear. Aims: To verify the conclusion of our previous study (Blood 2015. 126:858–62) regarding the lack of influence in the prognosis of abnormal serum free light chain ratios (sFLCr) in a more extensive series of newly diagnosed multiple myeloma (NDMM) patients in CR or sCR Methods: NDMM patients aged ≤65 years enrolled in GEM2012MENOS65 (NCT01916252). Of 458 patients, 8 discontinued early and were not evaluable. The definition of CR and sCR was made according to the IMWG criteria. SFLCr (FREELITE assay) was classified as normal (0.26–1.65) or abnormal (<0.26 if the patient was λ; >1.65 if the patient was κ). MRD was assessed using next-generation flow cytometry. The median limit of detection was of 3x10–6. All the CR with normal sFLCr and absence of clonal PCs by MFC with a sensitivity of 10–2 were considered sCR. Median follow-up was 47.5 months. Results: After induction, 425 patients were evaluable for response; 201 (47%) reached ≥CR. Data from sFLCr and MRD was available in 192 and 193 patients, respectively. In the CR evaluable patients for sCR assessment (n = 184), the rate of sCR was 64% (n = 117). In a landmark from the end of induction (median follow-up 39 months), sCR didn’t show significantly differences in PFS (2 years-PFS 89% vs 87%; P = .7) (fig 1a) neither in OS (2 years-OS 94% vs 94%; P = .9). Interestingly, patients with abnormal (n = 67) vs normal (n = 125) sFLCr showed superimposable PFS (2 years-PFS 88% vs 87%; P = .9) (fig 1b) and OS (2 years-OS 96% vs 94%; P = .8).By contrast, the few patients (n = 3) in whom BM clonality was detected by the low-sensitivity IHC-adapted MFC method had significantly worse PFS (2 years-PFS 89% vs 67%; P = .03) (fig 1c) and OS (2 years-OS 95% vs 67%; P = .8) than patients in whom BM clonality was undetectable or detected at levels below the threshold proposed for IHC assessment. On using our traditional MRD method, persistent MRD (>10–4) was detectable among 39 of the 193 (20%) patients who, compared with MRD-negative cases, had significantly inferior PFS (2-years PFS 91% vs 80%; P = .01) (fig 1b) and OS (2 years-OS 96% vs 87%; P = .01).As validation, we reproduced the analysis in the ASCT end-point. Of the 395 evaluable patients; 238 (60%) reached ≥CR. Once again, in the landmark analysis, sCR didn’t show significantly differences in PFS in respect to CR (P = .3) neither in OS (P = .6). Patients with abnormal (n = 51) vs normal (n = 173) sFLCr showed superimposable PFS (P = .9) and OS (P = .6). Patients with persistent MRD (>10–4), had significantly inferior PFS (P = .003) but similar OS (P = .2).Summary/Conclusion: These results confirm our previous analysis based on GEM05menos65/ GEM10mas65 clinical trials. For MM patients in CR, the stringent CR criteria does not predict a different outcome. If this essential criterion in the definition of sCR lacks for the prognosis connotations, it is not justified to maintain a response category whose real significance depends on the combination of the traditional CR criteria with a negative MRD status based on very low (IHC) or low resolution (<104) levels, today outdated.

Absence of Contribution to a Differential Outcome of the Stringent Complete Response IMWG Category Respect to the Conventional CR in Multiple Myeloma. a Validation Analysis Based on the Pethema/GEM2012MENOS65 Phase III Clinical Trial

Absence of Contribution to a Differential Outcome of the Stringent Complete Response IMWG Category Respect to the Conventional CR in Multiple Myeloma. a Validation Analysis Based on the Pethema/GEM2012MENOS65 Phase III Clinical Trial

Blind image deblurring based on multi-resolution ringing removal

Abstract In this paper, a blind deblurring algorithm that is well converged towards the latent sharp image and the blur PSF is proposed. The proposed algorithm employs our recently proposed ringing suppression regularizer in a solution to the blind deblurring problem. Latent sharp image and the blur PSF are estimated jointly via a multi-functional optimization problem addressed by a first-order primal-dual solver. This approach generates a multi-resolution pyramid of the input blurred image and propagates the estimation results of the lower resolution levels to the higher resolution ones. By considering the PSF initiation as an important step in the blind deblurring process, we introduce a PSF initiation method that benefits from the salient structure obtained via an l0-gradient smoothing of blurred image. A first-order primal-dual solution to the l0-gradient smoothing problem is introduced in this paper. Experimental results show that the proposed deblurring algorithm significantly enhances the results favorably against other state-of- the-art image deblurring methods.

An accurate and rapid continuous wavelet dynamic time warping algorithm for end-to-end mapping in ultra-long nanopore sequencing.

Long-reads, point-of-care and polymerase chain reaction-free are the promises brought by nanopore sequencing. Among various steps in nanopore data analysis, the end-to-end mapping between the raw electrical current signal sequence and the reference expected signal sequence serves as the key building block to signal labeling, and the following signal visualization, variant identification and methylation detection. One of the classic algorithms to solve the signal mapping problem is the dynamic time warping (DTW). However, the ultra-long nanopore sequencing and an order of magnitude difference in the sampling speed complexify the scenario and make the classical DTW infeasible to solve the problem. Here, we propose a novel multi-level DTW algorithm, continuous wavelet DTW (cwDTW), based on continuous wavelet transforms with different scales of the two signal sequences. Our algorithm starts from low-resolution wavelet transforms of the two sequences, such that the transformed sequences are short and have similar sampling rates. Then the peaks and nadirs of the transformed sequences are extracted to form feature sequences with similar lengths, which can be easily mapped by the original DTW. Our algorithm then recursively projects the warping path from a lower-resolution level to a higher-resolution one by building a context-dependent boundary and enabling a constrained search for the warping path in the latter. Comprehensive experiments on two real nanopore datasets on human and on Pandoraea pnomenusa demonstrate the efficiency and effectiveness of the proposed algorithm. In particular, cwDTW can gain remarkable acceleration with tiny loss of the alignment accuracy. On the real nanopore datasets, cwDTW can finish an alignment task in few seconds, which is about 3000 times faster than the original DTW. By successfully applying cwDTW on the tasks of signal labeling and ultra-long sequence comparison, we further demonstrate the power and applicability of cwDTW. Our program is available at https://github.com/realbigws/cwDTW. Supplementary data are available at Bioinformatics online.

Classification of image matching point clouds over an urban area

ABSTRACTAirborne laser scanning (ALS) and image matching are the two main techniques for generating point clouds for large areas. While the classification of ALS point clouds has been well investigated, there are few studies that are related to image matching point clouds. In this study, point clouds of multiple resolutions from high-resolution aerial images (ground sampling distance, GSD, of 6 cm) over the city of Vienna were generated and investigated with respect to point density and processing time. Three different study sites with various urban structures are selected from a bigger dataset and classified based on two different approaches: machine learning and a traditional operator-based decision tree. Classification accuracy was evaluated and compared with confusion matrices. In general, the machine learning method results in a higher overall accuracy compared to the simple decision tree method, with accuracies of 87% and 84%, respectively, at the highest resolution. At lower-resolution levels (GSDs of 12 cm and 24 cm), the overall accuracy of machine learning drops by 4% and that of the simple decision tree by 7% for each level. Classifying rasterized data instead of the original point cloud resulted in an accuracy drop of 5%. Thus, using machine learning on point clouds at the highest available resolution is suggested for classification of urban areas.

Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation

Doppler beam sharpening (DBS) is a critical technology for airborne radar ground mapping in forward-squint region. In conventional DBS technology, the narrow-band Doppler filter groups formed by fast Fourier transform (FFT) method suffer from low spectral resolution and high side lobe levels. The iterative adaptive approach (IAA), based on the weighted least squares (WLS), is applied to the DBS imaging applications, forming narrower Doppler filter groups than the FFT with lower side lobe levels. Regrettably, the IAA is iterative, and requires matrix multiplication and inverse operation when forming the covariance matrix, its inverse and traversing the WLS estimate for each sampling point, resulting in a notably high computational complexity for cubic time. We propose a fast IAA (FIAA)-based super-resolution DBS imaging method, taking advantage of the rich matrix structures of the classical narrow-band filtering. First, we formulate the covariance matrix via the FFT instead of the conventional matrix multiplication operation, based on the typical Fourier structure of the steering matrix. Then, by exploiting the Gohberg–Semencul representation, the inverse of the Toeplitz covariance matrix is computed by the celebrated Levinson–Durbin (LD) and Toeplitz-vector algorithm. Finally, the FFT and fast Toeplitz-vector algorithm are further used to traverse the WLS estimates based on the data-dependent trigonometric polynomials. The method uses the Hermitian feature of the echo autocorrelation matrix R to achieve its fast solution and uses the Toeplitz structure of R to realize its fast inversion. The proposed method enjoys a lower computational complexity without performance loss compared with the conventional IAA-based super-resolution DBS imaging method. The results based on simulations and measured data verify the imaging performance and the operational efficiency.

Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks.

Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures, current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging, where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge, we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging. This framework includes: (i) a novel preprocessing algorithm enhancing spines in a way that they are included in the binarized volume produced during the segmentation of foreground from background; (ii) the mathematical foundation of this algorithm, and (iii) an algorithm for the detection of spine locations in reference to centerline trace and separating them from the branches to whom spines are attached to. This framework enables the computation of a wide range of geometric features such as spine length, spatial distribution and spine volume in a high-throughput fashion. We illustrate our approach for the automated extraction of dendritic spine features in time-series multi-photon images of layer 5 cortical excitatory neurons from the mouse visual cortex.

Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation

The European Space Agency (ESA) is currently implementing the BIOMASS mission as 7th Earth Explorer satellite. BIOMASS will provide for the first time global forest aboveground biomass estimates based on P-band synthetic aperture radar (SAR) imagery. This paper addresses an often overlooked element of the data processing chain required to ensure reliable and accurate forest biomass estimates: accurate identification of forest areas ahead of the inversion of radar data into forest biomass estimates.The use of the P-band data from BIOMASS itself for the classification into forest and non-forest land cover types is assessed in this paper. For airborne data in tropical, hemi-boreal and boreal forests we demonstrate that classification accuracies from 90 up to 97% can be achieved using radar backscatter and phase information. However, spaceborne data will have a lower resolution and higher noise level compared to airborne data and a higher probability of mixed pixels containing multiple land cover types. Therefore, airborne data was reduced to 50m, 100m and 200m resolution. The analysis revealed that about 50–60% of the area within the resolution level must be covered by forest to classify a pixel with higher probability as forest compared to non-forest. This results in forest omission and commission leading to similar forest area estimation over all resolutions. However, the forest omission resulted in a biased underestimated biomass, which was not equaled by the forest commission. The results underline the necessity of a highly accurate pre-classification of SAR data for an accurate unbiased aboveground biomass estimation.

FRESH-FRI-Based Single-Image Super-Resolution Algorithm.

In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets. We achieve this by modeling images and, more precisely, lines of images as piecewise smooth functions and propose a resolution enhancement method for this type of functions. The method makes use of the theory of sampling signals with finite rate of innovation (FRI) and combines it with traditional linear reconstruction methods. We combine the two reconstructions by leveraging from the multi-resolution analysis in wavelet theory and show how an FRI reconstruction and a linear reconstruction can be fused using filter banks. We then apply this method along vertical, horizontal, and diagonal directions in an image to obtain a single-image super-resolution algorithm. We also propose a further improvement of the method based on learning from the errors of our super-resolution result at lower resolution levels. Simulation results show that our method outperforms state-of-the-art algorithms under different blurring kernels.

Hierarchical nucleus segmentation in digital pathology images.

Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.